The idea of label propagation is closely related to the idea of using pseudo-labels in semi-supervised classification tasks (see, e.g, [1]}, [2]}). When a trained classifier becomes confident in the predicted class of an unlabeled example, that example is added to the labeled set, which means that the label is propagated through the easiest examples in the unlabeled set. Model ensembles [3]} or prediction ensembles [4]} can be used in those tasks to generate better targets. In our algorithm, we use the confidence of the ensemble predictions to decide whether the solution interval can be extended and which points can be assigned pseudo-labels. Since PINNs are trained on real-valued targets, one can view PINNs as regression models regularized by the PDE penalty (REF ).
Label propagation in regression tasks is less studied with only a few existing works on semi-supervised regression [5]}, [6]}.
